Song personalized recommendation is one of the most popular applications in the current field of personalized recommendation. The range of listening songs can be enlarged by searching favorite songs for a user and recommending the songs to the user, and the user stickiness can be enhanced.
At present, the collaborative filtering recommendation method is generally used to recommend songs for the user, and does not need to obtain the characteristics of the user or the songs, but only depends on the user's past behaviors (such as browsing to the songs) to the songs. The feedbacks of the user on the songs can be collected in the form of scores, and the similarity degrees among the user behaviors will be calculated. The evaluation of neighbors with higher similarity degrees for other songs can be used to predict the degree of preference of a target user for a song to be recommended, and finally the song is recommended to the target user according to the degree of preference.
However, the collaborative filtering recommendation method at least has the problem that the styles of recommended songs are different, and the recommended songs may be less popular.